NCT01420016|CompletedResultsDMC
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Sponsor study identifier
Secondary identifiers
interventional
7,914
0 countries
N/A
Study Start
August 20, 2012
Actual (reported)Primary Completion
August 19, 2014
Actual (reported)Study Completion
August 19, 2014
Actual (reported)The objective of this project is to develop and implement sophisticated point-of-care Electronic Health Record (EHR)-based clinical decision support that (a) identifies and (b) prioritizes all available evidence-based treatment options to reduce a given patient's cardiovascular risk (CVR). After developing the EHR-based decision support intervention, the investigators will test its impact on CVR, the components of CVR, in a group randomized trial that includes 18 primary care clinics, 60 primary care physicians, and 18,000 adults with moderate or high CVR. This approach, if successful, will (a) improve chronic disease outcomes and reduce CVR for about 35% of the U.S. adult population, (b) maximize the clinical return on the massive investments that are increasingly being made in sophisticated outpatient EHR systems, and (c) provide a model for how to use EHR technology support to deliver "personalized medicine" in primary care settings
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
August 19, 2014
Actual (reported)Click on a node to explore related trials.
Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
January 17, 2011
Actual (reported)August 19, 2011
Actual (reported)August 20, 2012
Actual (reported)August 19, 2014
Actual (reported)August 19, 2014
Actual (reported)September 21, 2018
Actual (reported)Predicted Annual Rate of Change in 10-year Risk of Fatal or Nonfatal Heart Attack or Stroke
Ten year cardiovascular risk was calculated at each post index visit from the most recent clinical and laboratory values in the EMR. The Framingham lipid equation was used when a lipid value was available in the previous 5 years; otherwise the Framingham BMI equation was used. The primary outcome was the annualized rate of change (slope) in 10-year CVR, estimated for each treatment group from the time and time-by-treatment parameters of a mixed regression model which predicted post-index CVR values from time elapsed since index, treatment group and the time by treatment interaction.
Index to 14 months post index
The Prioritized Clinical Decision Support (CDS) intervention is a protocol driven CDS system linked within the EMR that identifies patients with high cardiovascular risk and provides tailored, prioritized decision support to the provider and patient at the point of care. The CDS was printed at intervention sites. It i) compiled most recent lab data (A1c, SBP, and LDL), BMI, smoking status, and aspirin use, (ii) calculated a 10-year risk for stroke or heart attack, (iii) prioritized clinical domains based on the absolute risk reduction for each component, (iv) compiled information related to renal and liver function, creatine kinase level, and previous diagnoses (CHF, CVD, DM), and (v) provided recommendations for intensification of therapy for A1c, SBP and/or LDL if not at goal.
Providers in the usual care arm did not have access to the prioritized clinical decision support tool.
Eighteen primary care clinics were blocked on size and on patient characteristics. Each clinic was randomly assigned to one of 2 study arms. All consenting PCPs were allocated to the study arm that their clinic was assigned to and the estimated 400 eligible adults with 10-year CVR \>= 10% under the care of each consenting physician were allocated to the same treatment arm as their PCP.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
Wolfson J, Vock DM, Bandyopadhyay S, Kottke T, Vazquez-Benitez G, Johnson P, Adomavicius G, O'Connor PJ. Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data. J Am Heart Assoc. 2017 Apr 24;6(4):e003670. doi: 10.1161/JAHA.116.003670.
O'Connor PJ, Sperl-Hillen JM, Fazio CJ, Averbeck BM, Rank BH, Margolis KL. Outpatient diabetes clinical decision support: current status and future directions. Diabet Med. 2016 Jun;33(6):734-41. doi: 10.1111/dme.13090.
Because this study was conducted at a single, relatively high-performing medical group, generalizability of results to other care delivery systems or patient populations is uncertain.
Patrick J O'Connor, MD, MPH, MA
HealthPartners Institute
7,914 participants (actual enrollment reported by the registry).
First Submitted
January 17, 2011
First Posted
August 19, 2011
Study Start
August 20, 2012
Actual (reported)Detecting and Treating High Blood Pressure in Aboriginal Population and Low and Middle Income Countries
Contact the study team to confirm eligibility.
O'Connor PJ, Sperl-Hillen JM, Margolis KL, Kottke TE. Strategies to Prioritize Clinical Options in Primary Care. Ann Fam Med. 2017 Jan;15(1):10-13. doi: 10.1370/afm.2027. Epub 2017 Jan 6. No abstract available.
Vock DM, Wolfson J, Bandyopadhyay S, Adomavicius G, Johnson PE, Vazquez-Benitez G, O'Connor PJ. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting. J Biomed Inform. 2016 Jun;61:119-31. doi: 10.1016/j.jbi.2016.03.009. Epub 2016 Mar 16.
Wolfson J, Bandyopadhyay S, Elidrisi M, Vazquez-Benitez G, Vock DM, Musgrove D, Adomavicius G, Johnson PE, O'Connor PJ. A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data. Stat Med. 2015 Sep 20;34(21):2941-57. doi: 10.1002/sim.6526. Epub 2015 May 18.
O'Connor PJ, Desai JR, Butler JC, Kharbanda EO, Sperl-Hillen JM. Current status and future prospects for electronic point-of-care clinical decision support in diabetes care. Curr Diab Rep. 2013 Apr;13(2):172-6. doi: 10.1007/s11892-012-0350-z.
Gilmer TP, O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL. Cost-effectiveness of an electronic medical record based clinical decision support system. Health Serv Res. 2012 Dec;47(6):2137-58. doi: 10.1111/j.1475-6773.2012.01427.x. Epub 2012 May 11.
O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL, Gilmer TP. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann Fam Med. 2011 Jan-Feb;9(1):12-21. doi: 10.1370/afm.1196.
O'Connor P. Opportunities to Increase the Effectiveness of EHR-Based Diabetes Clinical Decision Support. Appl Clin Inform. 2011 Aug 31;2(3):350-4. doi: 10.4338/ACI-2011-05-IE-0032. Print 2011.
Sperl-Hillen J, Margolis K, Crain L. Risk and Benefit Information and Use of Aspirin. JAMA Intern Med. 2017 Feb 1;177(2):291. doi: 10.1001/jamainternmed.2016.7988. No abstract available.
Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson G, Sharma R, Desai JR, O'Connor PJ. Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc. 2018 Sep 1;25(9):1137-1146. doi: 10.1093/jamia/ocy085.
Primary Completion
August 19, 2014
Actual (reported)Study Completion
August 19, 2014
Actual (reported)Last Updated
September 21, 2018
Results First Posted
September 21, 2018
Record last verified: 2015-02
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Source: ClinicalTrials.gov.
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